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    Digital image pre-processing and processing, including advanced processing techniques. Field data collection, image classification, and image enhancement. Students will produce a resource map and critically evaluate its accuracy based upon literature searches and field checks.

    The increasing number of imaging sensors and platforms offers new exciting capabilities to observe and characterise our environment. Dealing with imagery requires a specific set of skills that are addressed in this paper. This paper also provides an opportunity to gain practical experience with various types of imagery and to complete a comprehensive research project involving the use of images.

    About this paper

    Paper title Resource Mapping and Image Processing
    Subject Surveying
    EFTS 0.1334
    Points 18 points
    Teaching period Semester 2 (On campus)
    Domestic Tuition Fees ( NZD ) $1,206.20
    International Tuition Fees Tuition Fees for international students are elsewhere on this website.
    SURV 309 or SURV 318
    SURV 513 and SURV 424
    Schedule C
    Suitable for graduates and professionals of all disciplines with initial knowledge of remote sensing technologies and interested in getting advanced knowledge and experience in image processing techniques

    This paper is offered at the 500-level specifically to support postgraduate students aiming at using remote sensing in their research, as well as professionals seeking to gain new skills in geospatial sciences.
    Teaching staff
    Convenor and Lecturer: Dr Pascal Sirguey
    Paper Structure

    Generally based in the context of remote sensing, this paper is aimed at providing an extended knowledge of image processing techniques used for the mapping of earth resources. It includes classic methods of pre-processing (e.g. gap-filling, calibration, geometric and radiometric correction), image enhancement (e.g. radiometric, spatial, multispectral enhancement, principal component analysis) and image classification (unsupervised and supervised), as well as advanced processing techniques (e.g. multispectral image fusion, spectral unmixing, fuzzy and object-orientated classification). The methodology for fieldwork, sampling, ground-truthing and accuracy assessment of the final resource maps is addressed to provide students with sufficient knowledge towards the completion of deliverables with a high-quality standard.

    Image enhancement and manipulation techniques will be applied and analysed by the student to produce a resource map and critically evaluate its accuracy based upon a literature review and personal experiences.

    Teaching Arrangements
    The theoretical content of this paper is addressed over two hours of lectures weekly.

    Practical experience is gained during 11 practical sessions in a well-equipped computer laboratory, whereby students will be asked to carry out a research-led project that relies on imagery and image processing techniques. This is a major component of the paper.
    Recommended: Richards and Jia (2006). Remote sensing digital image analysis. An introduction. 4th Edition, Springer-Verlag, 363pp. (Electronic edition available online from the Dunedin Campus; older editions are available at the library)
    Graduate Attributes Emphasised
    Global perspective, Interdisciplinary perspective, Lifelong learning, Scholarship, Communication, Critical thinking, Environmental literacy, Information literacy, Research, Self-motivation, Teamwork.
    View more information about Otago's graduate attributes.
    Learning Outcomes

    Students who successfully complete the paper will:

    • Develop an extended understanding of image processing techniques
    • Gain the capacity to design an appropriate image processing protocol according to a specific problematic
    • Produce a thematic map from imagery
    • Be able to evaluate critically the accuracy of mapping outputs
    • Be able to deliver the results in a professional manner


    Semester 2

    Teaching method
    This paper is taught On Campus
    Learning management system


    Stream Days Times Weeks
    A1 Monday 12:00-12:50 29-35, 37-42
    Tuesday 13:00-13:50 29-35, 37-42


    Stream Days Times Weeks
    A1 Monday 14:00-16:50 29-35, 37-42
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